class_docstr: tests for BinaryCrossesntropy StrongConvex loss.
docstr: Test invalid domain for given params.
  
  Should return ValueError.
  
  Args:
    reg_lambda: initialization value for reg_lambda arg
    C: initialization value for C arg
    radius_constant: initialization value for radius_constant arg
docstr_tok: ["Test","invalid","domain","for","given","params",".","Should","return","ValueError",".","Args",":","reg_lambda",":","initialization","value","for","reg_lambda","arg","C",":","initialization","value","for","C","arg","radius_constant",":","initialization","value","for","radius_constant","arg"]
code: def test_bad_init_params(self, reg_lambda, C, radius_constant):
      """Test invalid domain for given params.
  
      Should return ValueError.
  
      Args:
        reg_lambda: initialization value for reg_lambda arg
        C: initialization value for C arg
        radius_constant: initialization value for radius_constant arg
      """
      # test valid domains for each variable
      with self.assertRaises(ValueError):
          StrongConvexBinaryCrossentropy(reg_lambda, C, radius_constant)
  
imports: from contextlib import contextmanager
  from io import StringIO
  import sys
  from absl.testing import parameterized
  import tensorflow.compat.v1
  from tensorflow.compat.v1.python.framework import test_util
  from tensorflow.compat.v1.python.keras import keras_parameterized
  from tensorflow.compat.v1.python.keras.regularizers import L1L2
  from tensorflow_privacy.privacy.bolt_on.losses import StrongConvexBinaryCrossentropy
  from tensorflow_privacy.privacy.bolt_on.losses import StrongConvexHuber
  from tensorflow_privacy.privacy.bolt_on.losses import StrongConvexMixin
label_desc: Use image node_id 2 for calling the BinaryCrossesntropyTests obj's underlying member method code with example usage: obj.test_bad_init_params(reg_lambda, C, radius_constant) without return types